Resveratrol in Postmenopausal Women With High Body Mass Index

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This pilot phase I trial studies resveratrol in postmenopausal women with high body mass index. Chemoprevention is the use of certain drugs to keep cancer from forming. The use of resveratrol may keep cancer from forming. Studying samples of blood and urine in the laboratory from postmenopausal women who are taking resveratrol may help doctors learn more about the effects of resveratrol on biomarkers.

Condition or disease

Intervention/treatment

Phase

Healthy, no Evidence of Disease

Drug: resveratrolOther: laboratory biomarker analysis

Phase 1

Detailed Description:

PRIMARY OBJECTIVES:

I. To determine the effect of pharmacological doses of resveratrol on serum estradiol levels in post-menopausal women with high body mass index (BMI).

II. Assess the effect of resveratrol on serum levels of insulin and C-peptide. III. Assess the effect of resveratrol on adipocytokine expression and secretion as measured by serum leptin and adiponectin.

IV. Assess the effect of resveratrol on inflammatory cytokines as measured by serum C-reactive protein (CRP).

Change in serum estradiol levels in postmenopausal women with high BMI [ Time Frame: From baseline to 12 weeks (post-intervention) ]

A two-sided paired t-test will be performed to determine whether the change is significant at a significance level of 5%. If the data distribution indicates non-normality or skewedness in violation of the assumptions of the t-test, non-parametric tests will be used. Linear regression techniques will be used to adjust for potential confounders, e.g. age and BMI.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Change in serum levels of insulin [ Time Frame: From baseline to 12 weeks (post-intervention) ]

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Change in serum levels of C-peptide [ Time Frame: From baseline to 12 weeks (post-intervention) ]

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Similar statistical analysis procedures as described for the primary endpoint will be performed to evaluate the changes of each of the endpoints at a significance level of 5%. Analysis will not be corrected for multiple comparisons but results will be interpreted cautiously. If the data distribution indicates non-normality or skewness, non-parametric tests will be used.

Incidence of reported adverse events [ Time Frame: Up to 12 weeks ]

Descriptive statistics of the type and frequency of all adverse events will be generated, including 95% confidence intervals.

Incidence of changes in CBC/diff, blood chemistry, and lipids [ Time Frame: Up to 12 weeks ]

Study agent/metabolite levels [ Time Frame: Up to 12 weeks ]

The Spearman correlation coefficient will be calculated to evaluate the correlation between biomarker changes and study agent/metabolite levels. Linear regression techniques will be used to adjust for potential confounders, e.g. age and BMI.

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